PAV and the ROC convex hull

Measuring the Accuracy of Diagnostic Systems

Swets, J. A. (1988) · Science · doi:10.1126/science.3287615 · Cited by 10,058 · see citing works

Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

Platt, J. C. (1999) · Cited by 4,834 · see citing works

An empirical comparison of supervised learning algorithms

Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780 · see citing works

Advances in Large-Margin Classifiers

(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,902 · see citing works

Predicting good probabilities with supervised learning

Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884 · see citing works

Transforming classifier scores into accurate multiclass probability estimates

Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280 · see citing works

Robust classification for imprecise environments

Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250 · see citing works

An Empirical Distribution Function for Sampling with Incomplete Information

Ayer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., & Silverman, E. (1955) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728423 · Cited by 896 · see citing works

Better Decisions through Science

Swets, J. A., Dawes, R. M., & Monahan, J. C. (2000) · Scientific American · doi:10.1038/scientificamerican1000-82 · Cited by 569 · see citing works

Learning and making decisions when costs and probabilities are both unknown

Zadrozny, B., & Elkan, C. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502540 · Cited by 456 · see citing works

Repairing concavities in ROC curves

Flach, P. A., & Wu, S. (2005) · Cited by 85 · see citing works

The Synergy Between PAV and AdaBoost

Wilbur, W. J., Yeganova, L. E., & Kim, W. B. (2005) · Machine Learning · doi:10.1007/s10994-005-1123-6 · Cited by 15 · see citing works

VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY

BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078

Verification of forecasts expressed in terms of probability

BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×

An experimental comparison of performance measures for classification

Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.08.010 · Cited by 889 · see citing works

Machine Learning: The art and science of algorithms that make sense of data

Flach, P. A. (2012) · Cited by 694 · see citing works

Precision-Recall-Gain Curves: PR Analysis Done Right

Flach, P. A., & Kull, M. (2015) · Bristol Research (University of Bristol) · doi:10.5555/2969239.2969333 · Cited by 289 · see citing works

Classifier calibration: a survey on how to assess and improve predicted class probabilities

Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · Machine Learning · doi:10.1007/s10994-023-06336-7 · Cited by 228 · see citing works

A unified view of performance metrics: translating threshold choice into expected classification loss

Hernández‐Orallo, J., Flach, P. A., & Ferri, C. (2012) · Bristol Research (University of Bristol) · Cited by 181 · see citing works

Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

Flach, P. A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019808 · Cited by 159 · see citing works

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

Hu, X., Wong, K., Young, G. S., Guo, L., & Wong, S. T. C. (2011) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22432 · Cited by 150 · see citing works

The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCFPreprint

Brümmer, N., & de Villiers, E. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.2865 · Cited by 165 · see citing works

Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration

Kull, M., Silva Filho, T. M., & Flach, P. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1338si · Cited by 111 · see citing works

Deconstructing Cross-Entropy for Probabilistic Binary Classifiers

Ramos, D., Franco-Pedroso, J., Lozano-Diez, A., & González-Rodríguez, J. (2018) · Entropy · doi:10.3390/e20030208 · Cited by 111 · see citing works

Information‐Theoretical Assessment of the Performance of Likelihood Ratio Computation Methods

Ramos, D., González-Rodríguez, J., Zadora, G., & Aitken, C. G. G. (2013) · Journal of Forensic Sciences · doi:10.1111/1556-4029.12233 · Cited by 102 · see citing works

Measuring, refining and calibrating speaker and language information extracted from speech

Brümmer, N. (2010) · SUNScholar (Stellenbosch University) · Cited by 94 · see citing works

Stable reliability diagrams for probabilistic classifiers

Dimitriadis, T., Gneiting, T., & Jordan, A. I. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016191118 · Cited by 84 · see citing works

Calibrating machine learning approaches for probability estimation: A comprehensive comparison

Ojeda, F. M., Jansen, M. L., Thiéry, A., Blankenberg, S., Weimar, C., Schmid, M., & Ziegler, A. (2023) · Statistics in Medicine · doi:10.1002/sim.9921 · Cited by 71 · see citing works

Score based procedures for the calculation of forensic likelihood ratios – Scores should take account of both similarity and typicality

Morrison, G. S., & Enzinger, E. (2017) · Science & Justice · doi:10.1016/j.scijus.2017.06.005 · Cited by 63 · see citing works

ROC Analysis

Flach, P. A. (2016) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7502-7_739-1 · Cited by 54 · see citing works

On Joint Optimization of Automatic Speaker Verification and Anti-Spoofing in the Embedding Space

Gomez-Alanis, A., González, J. A., Dubagunta, S. P., Peinado, A. M., & Magimai.-Doss, M. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3039045 · Cited by 45 · see citing works

A Simple Lexicographic Ranker and Probability Estimator

Flach, P. A., & Matsubara, E. (2007) · European Conference on Machine Learning · doi:10.1007/978-3-540-74958-5_55 · Cited by 56 · see citing works

Discrimination slope and integrated discrimination improvement – properties, relationships and impact of calibration

Pencina, M. J., Fine, J. P., & D’Agostino, R. B. (2016) · Statistics in Medicine · doi:10.1002/sim.7139 · Cited by 44 · see citing works

ROC curves in cost space

Hernández‐Orallo, J., Flach, P. A., & Ferri, C. (2013) · Machine Learning · doi:10.1007/s10994-013-5328-9 · Cited by 43 · see citing works

Binary Classifier Calibration Using an Ensemble of Near Isotonic Regression Models

Naeini, M. P., & Cooper, G. F. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0047 · Cited by 46 · see citing works

Cross‐view gait recognition by fusion of multiple transformation consistency measures

Muramatsu, D., Makihara, Y., & Yagi, Y. (2015) · IET Biometrics · doi:10.1049/iet-bmt.2014.0042 · Cited by 39 · see citing works

Receiver Operating Characteristic (ROC) CurvesPreprint

Gneiting, T., & Vogel, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04808 · Cited by 38 · see citing works

Multi-modal biometrics for mobile authentication

Aronowitz, H., Li, M., Ronen, O., Harary, S., Geva, A. B., Ben-David, S., Rendel, A., Hoory, R., Ratha, N., Pankanti, S., et al. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996269 · Cited by 30 · see citing works

Design principles of massive, robust prediction systems

Raeder, T., Stitelman, O., Dalessandro, B., Perlich, C., & Provost, F. J. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339740 · Cited by 30 · see citing works

Distribution Calibration for RegressionPreprint

Song, H., Diethe, T., Kull, M., & Flach, P. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06023 · Cited by 32 · see citing works

Calibration of medical diagnostic classifier scores to the probability of disease

Chen, W., Sahiner, B., Samuelson, F., Pezeshk, A., & Petrick, N. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280216661371 · Cited by 29 · see citing works

Accurate probability calibration for multiple classifiers

Zhong, W., & Kwok, J. T.-Y. (2013) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 28 · see citing works

A comparison of filtering evaluation metrics based on formal constraints

Amigó, E., Gonzalo, J. A., Verdejo, F., & Spina, D. (2019) · Information Retrieval · doi:10.1007/s10791-019-09355-y · Cited by 27 · see citing works

Bipartite ranking: a risk-theoretic perspective

Menon, A. K., & Williamson, R. C. (2016) · ANU Open Research (Australian National University) · Cited by 27 · see citing works

Probabilistic Confusion Entropy for Evaluating Classifiers

Wang, X., Wei, J., Han, J., Yu, G., & Zhang, H. (2013) · Entropy · doi:10.3390/e15114969 · Cited by 26 · see citing works

Intratumoral stromal morphometry predicts disease recurrence but not response to 5‐fluorouracil—results from the QUASAR trial of colorectal cancer

Hutchins, G. G. A., Treanor, D. E., Wright, A. I., Handley, K., Magill, L., Tinkler‐Hundal, E., Southward, K., Seymour, M., Kerr, D. J., Gray, R. G., et al. (2017) · Histopathology · doi:10.1111/his.13326 · Cited by 26 · see citing works

Calibration of Machine Learning Models

Bella, A., Ferri, C., Hernández‐Orallo, J., & Ramírez-Quintana, M. J. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch104 · Cited by 26 · see citing works

ROC Analysis

Flach, P. A. (2010) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_733 · Cited by 27 · see citing works

ShareBoost: Boosting for Multi-view Learning with Performance Guarantees

Peng, J., Barbu, C., Seetharaman, G. S., Fan, W., Wu, X., & Palaniappan, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_38 · Cited by 25 · see citing works

On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation

Narasimhan, H., & Agarwal, S. (2013) · neural information processing systems · Cited by 26 · see citing works

Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation

Gneiting, T., & Vogel, P. (2021) · Machine Learning · doi:10.1007/s10994-021-06115-2 · Cited by 69 · see citing works

Effect of calibration data on forensic likelihood ratio from a face recognition system

Ali, T., Spreeuwers, L., Veldhuis, R. N. J., & Meuwly, D. (2013) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2013.6712709 · Cited by 21 · see citing works

Classifier Calibration

Flach, P. A. (2016) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7502-7_900-1 · Cited by 21 · see citing works

Binary classifier calibration using an ensemble of piecewise linear regression models

Naeini, M. P., & Cooper, G. F. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1133-2 · Cited by 20 · see citing works

Measuring calibration of likelihood-ratio systems: A comparison of four metrics, including a new metric devPAV

Vergeer, P., van Schaaik, Y., & Sjerps, M. J. (2021) · Forensic Science International · doi:10.1016/j.forsciint.2021.110722 · Cited by 19 · see citing works

Efficient implementation of class-based decomposition schemes for Naïve Bayes

Park, S.-H., & Fürnkranz, J. (2013) · Machine Learning · doi:10.1007/s10994-013-5430-z · Cited by 18 · see citing works

Improving calibration of forensic glass comparisons by considering uncertainty in feature-based elemental data

Ramos, D., Maroñas, J., & Almirall, J. R. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104399 · Cited by 16 · see citing works

Evaluating probabilistic classifiers: The triptych

Dimitriadis, T., Gneiting, T., Jordan, A. I., & Vogel, P. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2023.09.007 · Cited by 18 · see citing works

From facial images of different quality to score based LR

Ruifrok, A. C. C., Vergeer, P., & Rodríguez, A. M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111201 · Cited by 15 · see citing works

Receiver Operating Characteristic (ROC) Analysis

Lachiche, N. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch255 · Cited by 15 · see citing works

Sampling variability in forensic likelihood-ratio computation: A simulation study

Ali, T., Spreeuwers, L., Veldhuis, R. N. J., & Meuwly, D. (2015) · Science & Justice · doi:10.1016/j.scijus.2015.05.003 · Cited by 14 · see citing works

A Comparison of Evaluation Metrics for Document Filtering

Amigó, E., Gonzalo, J. A., & Verdejo, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23708-9_6 · Cited by 12 · see citing works

Setting decision thresholds when operating conditions are uncertain

Ferri, C., Hernández‐Orallo, J., & Flach, P. A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00613-7 · Cited by 11 · see citing works

Likelihood ratio method for the interpretation of iPhone health app data in digital forensics

Vink, M. (., Sjerps, M. J., Boztas, A., & van Zandwijk, J. P. (2022) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2022.301389 · Cited by 11 · see citing works

A review of calibration methods for biometric systems in forensic applications

Ali, T., Spreeuwers, L., & Veldhuis, R. N. J. (2012) · University of Twente Research Information · Cited by 10 · see citing works

An overview of log likelihood ratio cost in forensic science – Where is it used and what values can we expect?

van Lierop, S., Ramos, D., Sjerps, M. J., & Ypma, R. J. F. (2024) · Forensic Science International Synergy · doi:10.1016/j.fsisyn.2024.100466 · Cited by 10 · see citing works

Reliably Calibrated Isotonic Regression

Nyberg, O., & Arto Klami, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_46 · Cited by 10 · see citing works

Classifier Calibration

Flach, P. A. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_900 · Cited by 10 · see citing works

Obtaining calibrated probability using ROC Binning

Sun, M., & Cho, S. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0578-3 · Cited by 8 · see citing works

Analyst and machine learning opinions in fire debris analysis

Whitehead, F. A., Williams, M. R., & Sigman, M. E. (2023) · Forensic Chemistry · doi:10.1016/j.forc.2023.100517 · Cited by 8 · see citing works

Georeferencing Flickr Photos Using Language Models at Different Levels of Granularity: An Evidence Based Approach

Laere, O. V., Schockaert, S., & Dhoedt, B. (2012) · Journal of Web Semantics · doi:10.1016/j.websem.2012.05.005 · Cited by 8 · see citing works

Biometric evidence evaluation: an empirical assessment of the effect of different training data

Ali, T., Spreeuwers, L., Veldhuis, R. N. J., & Meuwly, D. (2014) · IET Biometrics · doi:10.1049/iet-bmt.2014.0009 · Cited by 8 · see citing works

Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks

Zeinstra, C., Veldhuis, R. N. J., & Spreeuwers, L. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2746013 · Cited by 8 · see citing works

Development of a score-to-likelihood ratio model for facial recognition using authentic criminalistic data

Mölder, A. L., Astrom, I. E., & Leitet, E. (2020) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf49977.2020.9107954 · Cited by 8 · see citing works

A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts

Loveday, N., Taggart, R., & Khanarmuei, M. (2024) · Weather and Forecasting · doi:10.1175/waf-d-23-0201.1 · Cited by 7 · see citing works

Cost-Effective Hidden Markov Model-Based Image Segmentation

Lim, J., & Pyun, K. P. (2009) · IEEE Signal Processing Letters · doi:10.1109/lsp.2008.2008586 · Cited by 7 · see citing works

On classification, ranking, and probability estimation

Flach, P. A., & Matsubara, E. T. (2008) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dagsemproc.07161.8 · Cited by 7 · see citing works

The relation between the secrecy rate of biometric template protection and biometric recognition performance

Veldhuis, R. N. J. (2015) · International Conference on Biometrics · doi:10.1109/icb.2015.7139055 · Cited by 6 · see citing works

Smooth Receiver Operating Characteristics (smROC) Curves

Klement, W., Flach, P. A., Japkowicz, N., & Matwin, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_13 · Cited by 6 · see citing works

Mind the Gap: A Practical Framework for Classifiers in a Forensic Context

Zeinstra, C., Meuwly, D., Veldhuis, R. N. J., & Spreeuwers, L. (2018) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2018.8698583 · Cited by 6 · see citing works

Towards the automation of forensic facial individualisation : Comparing forensic to non forensic eyebrow features

Zeinstra, C., Veldhuis, R. N. J., & Spreeuwers, L. (2014) · University of Twente Research Information · Cited by 6 · see citing works

Smoke characterization for incipient wildfire detection from Sentinel-2 sensor based on sigmoid activation function and momentum gradient descent optimizer

Sali, A., Nomqupu, S., Nyamugama, A., & Ndou, N. N. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01991-x · Cited by 5 · see citing works

Discriminating Power of FISWG Characteristic Descriptors Under Different Forensic Use Cases

Zeinstra, C., Veldhuis, R. N. J., & Spreeuwers, L. (2016) · International Conference of the Biometrics Special Interest Group (BIOSIG) · doi:10.1109/biosig.2016.7736919 · Cited by 5 · see citing works

Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor

Nomqupu, S., Sali, A., Nyamugama, A., & Ndou, N. N. (2024) · Applied Sciences · doi:10.3390/app14072670 · Cited by 5 · see citing works

ROC convex hull and nonparametric maximum likelihood estimation

Lim, J., & Won, J. (2012) · Machine Learning · doi:10.1007/s10994-012-5290-y · Cited by 5 · see citing works

Quantile Regularization: Towards Implicit Calibration of Regression ModelsPreprint

Utpala, S., & Rai, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12860 · Cited by 5 · see citing works

Relações entre ranking, análise ROC e calibração em aprendizado de máquina

Matsubara, E. T. (2008) · doi:10.11606/t.55.2008.tde-04032009-114050 · Cited by 4 · see citing works

Improving classifier fusion via Pool Adjacent Violators normalization

Goswami, G., Ratha, N., Singh, R., & Mayank Vatsa, A. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899768 · Cited by 3 · see citing works

Non-convex isotonic regression via the Myersonian approach

Cui, Z., Lee, C., Zhu, L., & Zhu, Y. (2021) · Statistics & Probability Letters · doi:10.1016/j.spl.2021.109210 · Cited by 3 · see citing works

Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation

Flach, P. A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74958-5_2 · Cited by 3 · see citing works

Obtaining Accurate Probabilities Using Classifier Calibration.

Naeini, M. P. (2016) · ProQuest LLC eBooks · Cited by 3 · see citing works

Pruning of Rules and Rule Sets

Fürnkranz, J., Gamberger, D., & Nada Lavrač, A. (2012) · Cognitive technologies · doi:10.1007/978-3-540-75197-7_9 · Cited by 3 · see citing works

Non-Parametric Calibration of Probabilistic RegressionPreprint

Song, H., Kull, M., & Flach, P. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07690 · Cited by 3 · see citing works

Machine Learning Subjective Opinions: An Application in Forensic Chemistry

Akmeemana, A., & Sigman, M. E. (2025) · Algorithms · doi:10.3390/a18080482 · Cited by 2 · see citing works

Noble Ape's Cognitive Simulation

Barbalet, T. S. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-705-8.ch010 · Cited by 2 · see citing works

Pointwise exact bootstrap distributions of ROC curves

Dugas, C., & Gadoury, D. (2009) · Machine Learning · doi:10.1007/s10994-009-5134-6 · Cited by 2 · see citing works

Promoting transparency in forensic science by integrating categorical and evaluative reporting through decision theory

Sigman, M. E., & Williams, M. R. (2023) · Frontiers in Analytical Science · doi:10.3389/frans.2023.1105642 · Cited by 2 · see citing works

Utility-based regression and meta-learning techniques for modeling actual ET: Comparison to (METRIC-EEFLUX) model

Salem, F. K. A., Awad, S., Hamdar, Y., Kharroubi, S. A., & Jaafar, H. H. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.11.001 · Cited by 2 · see citing works

Investigation of methods for calibration of classifier scores to probability of disease

Chen, W., Sahiner, B., Samuelson, F. W., Pezeshk, A. X., & Petrick, N. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082142 · Cited by 2 · see citing works

Classifier calibration using splined empirical probabilities in clinical risk prediction

Gaudoin, R., Montana, G., Jones, S., Aylin, P. P., & Bottle, A. (2014) · Health Care Management Science · doi:10.1007/s10729-014-9267-1 · Cited by 2 · see citing works

In memory of Tom Fawcett

Provost, F. J. (2020) · Machine Learning · doi:10.1007/s10994-020-05909-0 · Cited by 2 · see citing works

Threshold Choice Methods: the Missing LinkPreprint

Hernández‐Orallo, J., Flach, P. A., & Ferri, C. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.2640 · Cited by 3 · see citing works

Pointwise exact bootstrap distributions of cost curves

Dugas, C., & Gadoury, D. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390192 · Cited by 2 · see citing works

Research on adversarial identification methods for AI ‐generated image software Craiyon V3

Jin, W., Luo, H., & Tang, Y. (2025) · Journal of Forensic Sciences · doi:10.1111/1556-4029.70034 · Cited by 1 · see citing works

An Alternative Approach for Evaluating the Binormal ROC Curve

Amala, R., & Vardhan, R. V. (2013) · International Journal of Green Computing · doi:10.4018/jgc.2013010101 · Cited by 1 · see citing works

Combining the advice of experts with randomized boosting for robust pattern recognition

Peng, J., & Seetharaman, G. S. (2013) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2013.6749332 · Cited by 1 · see citing works

A New Integral Representation of the Coverage Probability of a Random Convex Hull

Son, W., Ng, C. T., & Lim, J. (2015) · Communications for Statistical Applications and Methods · doi:10.5351/csam.2015.22.1.069 · Cited by 1 · see citing works

Improved landmine detection through context-dependent score calibration

Smock, B., Wilson, J. N., & Milner, M. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2224178 · Cited by 1 · see citing works

Classification of Ground-Truth Fire Debris Samples Using Neural Networks

Thurn, N. A. (2021) · Journal of International Crisis and Risk Communication Research · Cited by 1 · see citing works

An Approach to Machine Translation from Japanese Text to JSL Text

TAKAHASHI, K., TAKAMURA, H., & Okumura, M. (2008) · Journal of Natural Language Processing · doi:10.5715/jnlp.15.23 · Cited by 1 · see citing works

Model Integration in Data Mining: From Local to Global Decisions

Sanjuán, A. B. (2012) · doi:10.4995/thesis/10251/16964 · Cited by 1 · see citing works

ROC Analysis

Flach, P. A. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_739 · Cited by 191 · see citing works

Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves

Vardhan, R. V., & Balaswamy, S. (2013) · International Journal of Green Computing · doi:10.4018/jgc.2013070105

A hybrid approach to the fusion of partially coinciding alarms from asynchronous or specialized detectors

Smock, B., Wilson, J. N., & Glenn, T. C. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2054220

Classifier Calibration with ROC-Regularized Isotonic RegressionPreprint

Berta, E., Bach, F., & Jordan, M. I. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.12436 · Cited by 5 · see citing works

Classifier Calibration

Flach, P. A. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_900 · Cited by 10 · see citing works

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Accurate Probability Calibration for Multiple Classifiers

Zhong, L. W., James, Z., & Kwok, T.-Y. (2014)

Robust Probabilistic Calibration

Stefan Rüping, A. (2006)

ROC convex hull and nonparametric maximum likelihood estimation

Lim, J., & Won, J. (2012) · Machine Learning · doi:10.1007/s10994-012-5290-y · Cited by 5 · see citing works